Resilient Control of Cyber-Physical Systems with Distributed Learning - April 2019

PI(s) and Co-PI(s): Sayan Mitra and Geir Dullerud and Sanjay Shakkotai (U. Texas at Austin)

Researchers: Pulkit Katdare and Negin Musavi

HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.

Resiliency: Effective verification of safety and security properties of autonomous and cyber-physical systems

Metrics: How much data is necessary to achieve a certain level of confidence regarding a safety/security claim

PUBLICATIONS
Papers written as a result of your research from the current quarter only.

Project funding was finalized and approved in February for all participating universities. We have a working paper: Sample-optimal Verifiction of Markov Chains, Katdare, Musavi, Mitra, Shakkottai, and Dullerud, 2019, but have no additional publications to report at this time.

KEY HIGHLIGHTS
Each effort should submit one or two specific highlights. Each item should include a paragraph or two along with a citation if available. Write as if for the general reader of IEEE S&P.
The purpose of the highlights is to give our immediate sponsors a body of evidence that the funding they are providing (in the framework of the SoS lablet model) is delivering results that "more than justify" the investment they are making.

Two PhD students have been recruited and are dedicating their research time to the project. We have formulated a new direction of scientific enquiry into safety and security analysis of systems. The point of departure from existing literature is that we explore the relative value of data and models in assessing how well a system meets its requirements.

COMMUNITY ENGAGEMENTS

Nothing to report this quarter.

EDUCATIONAL ADVANCES

PI Mitra is designing and teaching a brand new course called Principles of Safe Autonomy. The course takes a deep dive into the seminal topics in object recognition, learning, localization, decision making, path planning, control, and safety verification. Around 25 students from ECE and CS are currently enrolled in this senior and graduate course. The course team has designed 6 New programming assignments involving topics such as lane detection, road-sign recognition with deep neural networks, localization with particle filters, decision making with reinforcement learning, path planning with rapidly expanding random trees, and safety verification using simulation-driven proofs. The students use a high-fidelity, commercial-grade vehicle simulator (Righthook) for testing their programming assignments. The university has recently acquired a electric GolfCart with LIDARS and cameras as a research platform and this platform will be made availebel to the students for their project. Galois Inc. has kindly agreed to sponsor prizes for student projects. Find out more about the safe autonomy course at https://publish.illinois.edu/safe-autonomy/